[RsR] use of lmrob() on ecological time series

[Stahel Werner A.]

Dear Emily


This is a late answer to your message from July 15.


The first issue is the use of robust linear regression of log(abundance) on the year.

I think that this is a very reasonable way to summarize the time series -- as long as

a log-linear trend appears appropriate.

The trend is then measured by the slope. A great advantage of using the log is that

slopes are then on a common scale for rare and abundant species, as a certain slope

corresponds to a certain percent increase or decrease per year.


You then go on to question the classification into significantly increasing, and so on.

This classification is common but unreasonable.

A 5% increase per year can be significant for one species and insignificant for another,

just because the former shows less random fluctuations than the latter.

We should focus on estimation and supply confidence intervals for characterizing the

(im-)precision.

(You may have read about the controversy about "null hypothesis significance testing"

and p values.)


Since the slopes are used for further analysis, the classification is not needed nor helpful

at all.

In any case, I have not read in detail what is done with the slope. In one paper, it is used

as the target variable in further regression models.

I wonder if such regressions make sense when different species are used in the same

regression. I thought it was a basic paradigm of biology that species have different

ways to react to environments.

If one simply want to show that management is helpful, one might compare managed and

non-managed regions in terms of the number of species (within taxonomic groups?) that

have recovered -- or directly in terms of average slopes for individual species or taxonomic

groups.


Nevertheless, let me add a thought about (2).

I think the expression "non-significant change" is quite appropriate since a change of 0

does not exist in real life. It is likely small (unless fluctuations are big and/or the time series

short, which causes in-significance), but never precisely 0.

Again, a confidence interval says it all: It contains all plausible values of the true slope.


Are these thoughts helpful?


Werner Stahel
M +41 79 784 9330 | P +41 44 364 6424
________________________________
Von: R-SIG-Robust <r-sig-robust-bounces using r-project.org> im Auftrag von Emily Klein <emily.klein04 using gmail.com>
Gesendet: Montag, 15. Juli 2019 21:34:45
An: r-sig-robust using r-project.org
Betreff: [RsR] use of lmrob() on ecological time series

Dear all,

I am using the lmrob() function from the robustbase package, and I have a
few questions. To keep the threads clear, I have a general inquiry here,
and will ask more specific Qs in a second thread. NB: I don't typically
update in the middle of a project, so am running on R version 3.4.1.

(1) I am curious the community's thoughts on our approach: We have several
hundred ecological time series and we're using robust linear models to
determine if the time series are increasing, decreasing, or not changing,
by looking at the modeled slope. This approach follows several others,
including Lotze et al. 2017 (doi: 10.1111/cobi.12957) and Magare et al.
2013 (doi:10.1371/journal.pone.0077908). I don't have much experience with
RLMs, so any thoughts on this approach would be very welcome.

More specifically, following the work noted above, we are running (with the
time series indexed with "DBx"):
lm_test<-lmrob(log(pop_status+1)~observation_year,DBx)

(2)  Previous use of RLMs to identify the direction of ecological time
series was asked in peer review to use "non-significant change" to
reference time series with a slope of zero within the 95% confidence
intervals. I can see excluding time series where there is no agreement on
the direction of slope, but I think that slope=0 is more "stable" or "no
change" and is not necessarily "non-significant". Any thoughts?

Thank you all very much for any feedback you may have. I will start a
second thread on a few warnings I am getting.

Emily

--
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Emily S. Klein, Senior Postdoctoral Associate (she / her / hers)
The Frederick S. Pardee Center for the Study of the Longer-Range Future |
Boston University
*Co-Chair*, ICES Working Group on the History of Fish & Fisheries (WGHIST)
esklein04 using gmail.com

http://www.bu.edu/pardee/
http://www.ices.dk/community/groups/Pages/WGHIST.aspx

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